Artificial intelligence has become the catchphrase of 2023, igniting a global debate on the pros and cons of the disruptive technology and the impact it could have on humans. However, few (if any) industries can afford to ignore AI and its transformative benefits – the banking sector included.
While financial institutions started using automated processes as far back as the 1960s, when cash machines were introduced, the power of AI is set to transform core banking systems in today’s digital age. Indeed, banks are increasingly looking to switch from inefficient, costly legacy systems to the agility of AI-driven core banking to reduce costs, enhance the customer experience, offer hyper-personalized products and services, and boost revenue and profit growth.
The Covid-19 pandemic has accelerated banks’ digital transformation over the past few years. Still, the sector faces numerous challenges today, particularly from Big Tech, which has access to valuable first-party data, and other fintech players entering the financial services sector. Meanwhile, a global survey by The Economist Intelligence Unit in 2020 found that 77% of bankers believe that the adoption of AI will be the differentiator between winning and losing banks.
Here, we explain why AI-driven core banking systems are the future of banking.
The evolution of legacy systems in banking
Banks have relied on legacy technology for decades to power their banking systems. These aging systems are difficult to maintain, and building new features on top of them is challenging and expensive. This has led many banks to adopt “out-of-the-box” software products pre-built with features such as reporting tools or customer relationship management solutions.
The packaged solutions offer some benefits over building from scratch, such as reduced risk due to vendor lock-in, but they often lack flexibility compared to custom-built core banking systems because they are designed for broad use cases.
In the early days, legacy systems were fairly straightforward in their scope: They performed basic, manual tasks such as managing customer accounts and paying bills by check or direct debit. However, over time, core banking has evolved into a complex system that includes many modules that work together to provide customers with services such as online bill paying, “on-the-go” mobile banking apps, and online account access via computers or smartphones.
In addition to providing these services directly through traditional channels such as cash machines, many banks now offer them through partnerships with third parties such as PayPal and Venmo.
According to a report by IBM, “Accelerating AI & Innovation: The future of banking depends on core modernization,” banks must modernize their core systems to deliver seamless experiences, leverage emerging technologies and remain competitive.
The rise of AI in core banking systems
AI has been used in banking since the 1960s, when banks began to automate routine tasks such as teller transactions and customer service with automated teller machines (ATMs). Electronic payments using cards emerged in the 1970s, and online banking became more prevalent in the 2000s, with customers accessing their accounts 24/7. The 2010s witnessed the rise of mobile-based banking, making it possible for people to bank on the go.
Today, AI technologies such as machine learning and natural language processing have become more sophisticated and can perform a wide range of complex tasks – from risk management and fraud detection to automated customer service inquiries. The growing adoption of these technologies in core banking operations enables enhanced customer experiences, and data-driven decision-making that results in higher employee productivity levels while reducing costs associated with manual processes.
According to a report by global consultancy McKinsey & Company titled “AI bank of the future: Can banks meet the AI challenge?”, using these technologies can result in increased automation and, if the associated risks are accounted for, can often surpass human decision-making in terms of both speed and precision. The potential for creating value through AI is also difficult to ignore: AI can unlock up to $1 trillion of additional value for banks annually, McKinsey adds.
How AI is used to streamline different aspects of core banking
Although the definition of core banking systems has remained largely unchanged over time – essentially, it’s back-office software that handles accounts and loans – customers’ expectations have evolved quickly.
Nowadays, many people expect their bank’s website or mobile app to offer an easy-to-use and consistent experience regardless of where they are. By embracing digital transformation and moving to an AI-driven core banking system, banks can provide personalized services more efficiently, create new revenue streams with new products, and enhance customer experiences while reducing costs.
To that end, AI advancements are already occurring in banking, such as predicting customer behavior, detecting fraudulent transactions, and automating processes. It’s also being used for improving customer service through chatbots that are available 24/7.
Banks use AI to make loan and credit decisions, track market trends, collect and analyze data, deal with regulatory compliance and risk management, and for predictive analysis. For example, banks are protecting customers against fraud and money laundering by using biometrics, while AI is analyzing customer data and predicting their preferences to offer products and services that address their needs more effectively.
AI is automating repetitive tasks that may be time-consuming for employees, freeing up their time so they can focus on other tasks that require human interaction. It’s being deployed to boost operational efficiency through real-time insights into business operations, financial performance, and better risk management capabilities through automated decision-making processes.
This includes flagging suspicious transactions faster than humans could do – leading to quicker response times and fewer false positives (which means less customer frustration).
Challenges for banks
Several challenges must be considered as banks move towards AI-driven core banking systems. One of the biggest is finding the right balance between human interaction and automation. While AI can help automate processes and provide valuable insights, it’s still in its infancy and may not always yield accurate results.
As such, banks need to ensure that their business has proper processes in place. In order to ensure that any mistakes made by an automated system can be corrected by a human, who understands what went wrong with the algorithm or model behind it.
Another concern is security: Cyber-attacks on financial institutions have become more sophisticated, particularly since the Covid-19 pandemic, as hackers try new ways to infiltrate systems and steal sensitive data such as credit card numbers or bank account details.
Banks should, therefore, consider implementing security measures such as encryption technology on all communications. Between customers’ devices, servers within their networks, third-party vendors providing related services, such as cloud storage services, and external entities outside those networks, including internet service providers.
According to a report by Appinvetiv on “How artificial intelligence is used in banks,” there is also the risk of biases learned from previous cases of poor human judgment. Appinvetiv adds that minor inconsistencies in AI systems can escalate and create large-scale problems, risking the bank’s reputation and functioning.
As AI becomes more prevalent, it will play an increasingly important role in the future of banking. The technology will change how we think about banking and improve the customer experience by reducing costs and increasing efficiency for banks, allowing them to be more competitive.
While there are still some barriers to overcome before banks fully integrate AI into their core banking systems, we believe these challenges will be overcome, and it will become an integral part of the future of banking.
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